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Synchronization and anti-synchronization analysis of discrete-time neural networks with delays

  • Kashkynbayev, Ardak (PI)
  • Ganesan, Soundararajan (Other Faculty/Researcher)
  • Issakhanov, Alfarabi (Other Faculty/Researcher)
  • Otkel, Madina (Other Faculty/Researcher)
  • Turganov, Alkham (Other Faculty/Researcher)

Project: Government

Project Details

Grant Program

Grant funding 2022-2024

Project Description

The main aim of the project is to understand complex behavior of uncertain discrete-time neural networks time delay. We will study synchronization and anti-synchronization of discrete-time neural networks with delays via output-feedback control strategy.

Project Impact

This research proposal aims at making substantial contribution to analyze synchronization and anti-synchronization problems for delayed discrete-time neural networks. To propose the synchronization and anti-synchronization criteria for the desired drive-response system model, several delay-dependent sufficient conditions will be derived in the form of linear matrix inequality (LMI) under which the estimated error dynamics are exponentially and asymptotically stable. Further, simulation examples with specific numeric parameters will be explored to confirm the applicability and assurance of the proposed theoretical results.
AcronymAP13068282
StatusFinished
Effective start/end date1/1/22 → 12/31/24

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